Highly precise risk prediction model for new-onset hypertension using artificial intelligence techniques

Machine learning-based analyses of contributing factors for the development of hypertension: a comparative study

OBJECTIVES

Sufficient attention has not been given to machine learning (ML) models using longitudinal data for investigating important predictors of new onset of hypertension. We investigated the predictive ability of several ML models for the development of hypertension.

METHODS

A total of 15 965 Japanese participants (men/women: 9,466/6,499, mean age: 45 years) who received annual health examinations were randomly divided into a training group (70%, n = 11,175) and a test group (30%, n = 4,790). The predictive abilities of 58 candidates including fatty liver index (FLI), which is calculated by using body mass index, waist circumference and levels of γ-glutamyl transferase and triglycerides, were investigated by statistics analogous to the area under the curve (AUC) in receiver operating characteristic curve analyses using ML models including logistic regression, random forest, naïve Bayes, extreme gradient boosting and artificial neural network.

RESULTS

During a 10-year period (mean period: 6.1 years), 2,132 subjects (19.1%) in the training group and 917 subjects (19.1%) in the test group had new onset of hypertension. Among the 58 parameters, systolic blood pressure, age and FLI were identified as important candidates by random forest feature selection with 10-fold cross-validation. The AUCs of ML models were 0.765-0.825, and discriminatory capacity was significantly improved in the artificial neural network model compared to that in the logistic regression model.

CONCLUSIONS

The development of hypertension can be simply and accurately predicted by each ML model using systolic blood pressure, age and FLI as selected features. By building multiple ML models, more practical prediction might be possible.

Prediction of incidence of hypertension in France and associated factors: results from the CONSTANCES cohort

INTRODUCTION

Estimating hypertension incidence and improving screening in general population could enhance blood pressure control and decrease cardiometabolic risks. Identifying those likely to develop hypertension is essential. Our study focused on predicting onset hypertension and its incidence based on initial characteristics.

METHODS

We utilized data from the French prospective CONSTANCES cohort, including volunteers assessed twice over 5 years up to 31 December 2019, who were initially free from hypertension. Hypertension was defined as having a SBP at least 140 mmHg or DBP at least 90 mmHg during the second checkup or if antihypertensive medication was prescribed. We calculated annual incidence rates among subgroups and used machine learning models to identify predictors of hypertension. The impact of changes in BMI was analyzed using logistic regression.

RESULTS

Of the 11 112 participants (average age 47.5 ± 12 years), 1929 (17.4%) developed hypertension within an average of 5.2 years, with 383 on medication. The incidence rate was 3.4 new cases per 100 person-years, rising with age and consistently higher in men (4.3 vs. 2.8). A blood pressure (BP) threshold of 130 mmHg predicted 70% of new cases. One-point BMI reduction significantly reduced hypertension risk by 16%, regardless of initial BMI and SBP levels.

CONCLUSION

The study reports a notable hypertension incidence of 3.4 new cases per 100 person-years, particularly among those with SBP over 130 mmHg, highlighting the need for regular screening. Early diagnosis and control can mitigate hypertension's adverse effects, emphasizing the crucial role of preventive measures like BMI reduction.

Artificial intelligence in retinal image analysis for hypertensive retinopathy diagnosis: a comprehensive review and perspective

Hypertensive retinopathy (HR) occurs when the choroidal vessels, which form the photosensitive layer at the back of the eye, are injured owing to high blood pressure. Artificial intelligence (AI) in retinal image analysis (RIA) for HR diagnosis involves the use of advanced computational algorithms and machine learning (ML) strategies to recognize and evaluate signs of HR in retinal images automatically. This review aims to advance the field of HR diagnosis by investigating the latest ML and deep learning techniques, and highlighting their efficacy and capability for early diagnosis and intervention. By analyzing recent advancements and emerging trends, this study seeks to inspire further innovation in automated RIA. In this context, AI shows significant potential for enhancing the accuracy, effectiveness, and consistency of HR diagnoses. This will eventually lead to better clinical results by enabling earlier intervention and precise management of the condition. Overall, the integration of AI into RIA represents a considerable step forward in the early identification and treatment of HR, offering substantial benefits to both healthcare providers and patients.

Expert consensus document on artificial intelligence of the Italian Society of Cardiology

Artificial intelligence (AI), a branch of computer science focused on developing algorithms that replicate intelligent behaviour, has recently been used in patients management by enhancing diagnostic and prognostic capabilities of various resources such as hospital datasets, electrocardiograms and echocardiographic acquisitions. Machine learning (ML) and deep learning (DL) models, both key subsets of AI, have demonstrated robust applications across several cardiovascular diseases, from the most diffuse like hypertension and ischemic heart disease to the rare infiltrative cardiomyopathies, as well as to estimation of LDL cholesterol which can be achieved with better accuracy through AI. Additional emerging applications are encountered when unsupervised ML methodology shows promising results in identifying distinct clusters or phenotypes of patients with atrial fibrillation that may have different risks of stroke and response to therapy. Interestingly, since ML techniques do not analyse the possibility that a specific pathology can occur but rather the trajectory of each subject and the chain of events that lead to the occurrence of various cardiovascular pathologies, it has been considered that DL, by resembling the complexity of human brain and using artificial neural networks, might support clinical management through the processing of large amounts of complex information; however, external validity of algorithms cannot be taken for granted, while interpretability of the results may be an issue, also known as a "black box" problem. Notwithstanding these considerations, facilities and governments are willing to unlock the potential of AI in order to reach the final step of healthcare advancements while ensuring that patient safety and equity are preserved.

Exploring the integration of medical and preventive chronic disease health management in the context of big data

Chronic non-communicable diseases (NCDs) pose a significant global health burden, exacerbated by aging populations and fragmented healthcare systems. This study employs a comprehensive literature review method to systematically evaluate the integration of medical and preventive services for chronic disease management in the context of big data, focusing on pre-hospital risk prediction, in-hospital clinical prevention, and post-hospital follow-up optimization. Through synthesizing existing research, we propose a novel framework that includes the development of machine learning models and interoperable health information platforms for real-time data sharing. The analysis reveals significant regional disparities in implementation efficacy, with developed eastern regions demonstrating advanced closed-loop management via unified platforms, while western rural areas struggle with manual workflows and data fragmentation. The integration of explainable AI (XAI) and blockchain-secured care pathways enhances clinical decision-making while ensuring GDPR-compliant data governance. The study advocates for phased implementation strategies prioritizing data standardization, federated learning architectures, and community-based health literacy programs to bridge existing disparities. Results show a 30-35% reduction in redundant diagnostics and a 15-20% risk mitigation for cardiometabolic disorders through precision interventions, providing a scalable roadmap for resilient public health systems aligned with the "Healthy China" initiative.

Intelligent risk stratification of hypertension based on ambulatory blood pressure monitoring and machine learning algorithms

Objective. Risk stratification of hypertension plays a crucial role in the treatment decisions and medication guidance during clinical practices. Although fruitful achievements have been reported on risk stratification of hypertension, the potential use of ambulatory blood pressure monitoring data is not well investigated. Different from single measuring blood pressure data, long-term blood pressure monitoring data can provide more comprehensive dynamical blood pressure information. Therefore, this paper proposes an intelligent hypertension risk stratification method based on ambulatory blood pressure monitoring data and improved machine learning algorithms.Approach. A total of 262 patients with hypertension are enrolled at People's Hospital of Yangjiang, in which 93 subjects are with simple hypertension and 169 subjects have hypertension with complication. Time-domain features, frequency-domain features, nonlinear dynamics features and correlation features underlying time-varying ambulatory blood pressure monitoring data are extracted to obtain discriminative feature representations. Synthetic minority over-sampling algorithm is applied to solve the problem of data balancing. The particle swarm optimization combined with kernel extreme learning machine is employed for feature fusion and optimization.Main results. The proposed method can yield a diagnostic accuracy of 93.7%, 97.8%, and 98.4% under two-, five- and ten-fold cross-validation, which demonstrates hypertension risk stratification in an intuitive, quantizable manner using multi-dimensional feature representation and learning.Significance. The proposed method is expected to provide early warning for latent serious cardiovascular diseases before obvious symptoms are present.

Construction of a predictive model for type 2 diabetes mellitus with coexisting hypertension: A cross-sectional study

Type 2 diabetes mellitus (T2DM) and hypertension often coexist, raising the risk of cardiovascular events, renal disease, and mortality. Early identification of high-risk patients with T2DM and concurrent HTN is vital for personalized care. This study aims to construct and validate a predictive model for hypertension in T2DM patients to aid early intervention and tailored treatment. A quantitative observational study using multivariable logistic regression analysis was conducted, with results presented in a nomogram. Data from 423 T2DM patients (206 with hypertension and 217 without) hospitalized at a tertiary hospital in Anhui Province between February 2023 and February 2024 were analyzed. Univariate and multivariate logistic regression identified significant predictors, and model performance was evaluated via ROC curves, AUC values, and the Hosmer-Lemeshow test. Age, alcohol use, diabetic nephropathy, coronary heart disease, cerebral infarction, and body mass index were significant predictors. The model showed good performance with an AUC of 0.72, and the Hosmer-Lemeshow test (P = .074) confirmed its fit. The predictive model effectively identifies high-risk T2DM patients for hypertension, aiding early intervention and personalized treatment.

Transforming undergraduate dental education: the impact of artificial intelligence

Artificial intelligence (AI) is a rapidly evolving area, having had a transformative effect within some areas of medicine and dentistry. In dentistry, AI systems are contributing to clinical decision-making, diagnostics and treatment planning. Ongoing advances in AI technology will lead to further expansion of its existing applications and more widespread use within the field of dentistry. The predicted transformation of current practice within dentistry brought about by AI necessitates the education of undergraduate dental students on the topic of AI. AI will act as a complementary tool to clinical expertise and skill, rather than a replacement. AI education frameworks should integrate with existing traditional dental education. It is important that dental students develop a sound understanding of the ethical considerations surrounding AI, ensuring its safe and responsible use. This article aims to consider the status of current undergraduate dental education and the possible impact of AI, examining both the challenges and opportunities, and the practicalities, of incorporating AI into existing undergraduate dental curricula. Understanding and envisioning the future landscape shaped by AI will allow for development of a future dental workforce who are skilled, confident and responsible AI users.

Machine learning evaluation of a hypertension screening program in a university workforce over five years

The global prevalence of hypertension continues excessively elevated, especially among low- and middle-income nations. Workplaces provide tremendous opportunities as a unique, easily accessible and practical avenue for early diagnosis and treatment of hypertension among the workforce class. The evaluation of such a Workplace Screening Strategy can give insight into its possible effects. Innovative machine learning approaches like k-means clustering are underutilized for such assessments. We set out to use this technology to analyze the results of our university's yearly health checkup of the employees for hypertension. An anonymized dataset including the demographics and blood pressure monitoring information gathered from workers in various departments/units of a learning organization. The overall amount of samples or data values is 1, 723, and the supplied dataset includes six attributes, such as year group (2018, 2019, 2021, 2022), Department/Unit (academic and non-academic), and gender (male and female), with the intended output being the blood pressure status (low, normal, and high). The dataset was analyzed using machine learning approaches. In this longitudinal study, it was discovered that the average age for the workforce is 42. Similarly, it was revealed that hypertension was common among employees over the age of 40, regardless of gender or occupational type (academic or nonacademic). The data also found that there was a consistent drop in the prevalence of hypertension from 2018 to 2022. According to the study findings, the use of machine learning algorithms for periodic evaluations of workplace health status monitoring initiatives (particularly for hypertension) is feasible, realistic, and sustainable in diagnosing and controlling hypertension among those in the workforce.

A machine learning analysis of predictors of future hypertension in a young population

BACKGROUND

Early diagnosis of hypertension (HT) is crucial for preventing end-organ damage. This study aims to identify the risk factors for future HT in young individuals through the application of machine learning (ML) models.

METHODS

The study included individuals aged 18-40 years who had not been diagnosed with HT through ambulatory blood pressure monitoring (ABPM). These participants were monitored for hypertension diagnosis from the date of ABPM application until the date of data collection. Hypertension prediction was carried out using three distinct ML methods: Support Vector Machine, Random Forest, and Least Absolute Shrinkage and Selection Operator. The identification of variables significant for future HT was based on the outcomes of these models.

RESULTS

This study comprised 516 patients, with a mean follow-up duration of 793.4±58.6 days. Following the integration of demographic data, laboratory results, and ABPM findings into the ML models, age, high-density lipoprotein cholesterol, triglycerides, and the standard deviation of systolic blood pressure (SDsis) were identified as predictors for future HT. A logistic regression with the selected variables (age, diabetes mellitus history, HDL, triglycerides, white blood cell count, and SDsis) using the full data set gave the following log odds 0.0737 (P<0.001), 0.7146 (P<0.001), -0.0160 (P=0.071), 0.0026 (P=0.002), 0.0857 (P=0.069), and 0.0850 (P=0.005), respectively. The corresponding probability values of age, diabetes mellitus history, HDL, triglycerides, white blood cell count, and SDsis were 0.5184, 0.6714, 0.4960, 0.5006, 0.5214, and 0.5212, respectively. This indicates a unit increase in all factors, except diabetes mellitus history, increases the probability of future HT by 50%. A history of diabetes, however, increases the probability of future HT by more than two thirds. The history of diabetes mellitus emerged as the most crucial predictor of future HT across all applied methods.

CONCLUSIONS

ML methods appear to be valuable tools for predicting future HT. The widespread adoption of these methods and the refinement of more comprehensive models will lay the groundwork for future studies.

Next-visit prediction and prevention of hypertension using large-scale routine health checkup data

This paper proposes the use of machine learning models to predict one's risk of having hypertension in the future using their routine health checkup data of their current and past visits to a health checkup center. The large-scale and high-dimensional dataset used in this study comes from MJ Health Research Foundation in Taiwan. The training data for models is separated into 5 folds and used to train 5 models in a 5-fold cross validation manner. While predicting the results for the test set, the voted result of 5 models is used as the final prediction. Experimental results show that our models achieve 69.59% of precision, 77.90% of recall, and 73.51% of F1-score, which outperforms a baseline using only the blood pressure of visitors' last visits. Experiments also show that a visitor who performs a health checkup more often can be predicted better, and models trained with selected important factors achieve better results than those trained with Framingham risk score. We also demonstrate the possibility of using our models to suggest visitors for weight control by adding virtual visits that assume their body weight can be reduced in the near future to model input. Experimental results show that around 5.48% of the people who are with high Body Mass Index of the true positive cases are rejudged as negative, and a rising trend appears when adding more virtual visits, which may be used to suggest visitors that controlling their body weight for a longer time lead to lower probability of having hypertension in the future.

Digitalization of hypertension management: a paradigm shift

Hypertension, which stands as a leading global health challenge, demands a dynamic approach for its effective management. The traditional methods of managing hypertension, centered on periodic clinic visits for blood pressure measurement and pharmacological interventions, are increasingly being complemented and enhanced by digital technologies. The integration of wearable devices, mobile applications, personalized treatments, and telehealth solutions into healthcare system is reshaping traditional hypertension care. Digitalization of hypertension management extends to population health, in addition to individual patient benefits, aimed at preventing and controlling hypertension on a broader scale. However, this digital revolution in hypertension management brings forth challenges related to data security, data accuracy, equitable access, and standardization of devices by international regulatory agencies. Addressing these issues is equally important to ensure that the benefits of digital technologies are accessible to everyone, irrespective of socio-economic factors. This paper concludes with a forward-looking perspectives, emphasizing the potential of digitalization to modify the landscape of hypertension management.

Predicting new cases of hypertension in Swedish primary care with a machine learning tool

Background

Many individuals with hypertension remain undiagnosed. We aimed to develop a predictive model for hypertension using diagnostic codes from prevailing electronic medical records in Swedish primary care.

Methods

This sex- and age-matched case-control (1:5) study included patients aged 30-65 years living in the Stockholm Region, Sweden, with a newly recorded diagnosis of hypertension during 2010-19 (cases) and individuals without a recorded hypertension diagnosis during 2010-19 (controls), in total 507,618 individuals. Patients with diagnoses of cardiovascular diseases or diabetes were excluded. A stochastic gradient boosting machine learning model was constructed using the 1,309 most registered ICD-10 codes from primary care for three years prior the hypertension diagnosis.

Results

The model showed an area under the curve (95 % confidence interval) of 0.748 (0.742-0.753) for females and 0.745 (0.740-0.751) for males for predicting diagnosis of hypertension within three years. The sensitivity was 63 % and 68 %, and the specificity 76 % and 73 %, for females and males, respectively. The 25 diagnoses that contributed the most to the model for females and males all exhibited a normalized relative influence >1 %. The codes contributing most to the model, all with an odds ratio of marginal effects >1 for both sexes, were dyslipidaemia, obesity, and encountering health services in other circumstances.

Conclusions

This machine learning model, using prevailing recorded diagnoses within primary health care, may contribute to the identification of patients at risk of unrecognized hypertension. The added value of this predictive model beyond information of blood pressure warrants further study.

Precise risk-prediction model including arterial stiffness for new-onset atrial fibrillation using machine learning techniques

Atrial fibrillation (AF) is the most common clinically significant cardiac arrhythmia and is an important risk factor for ischemic cerebrovascular events. This study used machine learning techniques to develop and validate a new risk prediction model for new-onset AF that incorporated the use electrocardiogram to diagnose AF, data from participants with a wide age range, and considered hypertension and measures of atrial stiffness. In Japan, Industrial Safety and Health Law requires employers to provide annual health check-ups to their employees. This study included 13 410 individuals who underwent health check-ups on at least four successive years between 2005 and 2015 (new-onset AF, n = 110; non-AF, n = 13 300). Data were entered into a risk prediction model using machine learning methods (eXtreme Gradient Boosting and Shapley Additive Explanation values). Data were randomly split into a training set (80%) used for model construction and development, and a test set (20%) used to test performance of the derived model. The area under the receiver operator characteristic curve for the model in the test set was 0.789. The best predictor of new-onset AF was age, followed by the cardio-ankle vascular index, estimated glomerular filtration rate, sex, body mass index, uric acid, γ-glutamyl transpeptidase level, triglycerides, systolic blood pressure at cardio-ankle vascular index measurement, and alanine aminotransferase level. This new model including arterial stiffness measure, developed with data from a general population using machine learning methods, could be used to identify at-risk individuals and potentially facilitation the prevention of future AF development.

Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years

BACKGROUND

Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology.

RESULTS

We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology.

CONCLUSIONS

The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.

Using Machine Learning to Evaluate the Value of Genetic Liabilities in the Classification of Hypertension within the UK Biobank

Background and Objective: Hypertension increases the risk of cardiovascular diseases (CVD) such as stroke, heart attack, heart failure, and kidney disease, contributing to global disease burden and premature mortality. Previous studies have utilized statistical and machine learning techniques to develop hypertension prediction models. Only a few have included genetic liabilities and evaluated their predictive values. This study aimed to develop an effective hypertension classification model and investigate the potential influence of genetic liability for multiple risk factors linked to CVD on hypertension risk using the random forest and the neural network. Materials and Methods: The study involved 244,718 European participants, who were divided into training and testing sets. Genetic liabilities were constructed using genetic variants associated with CVD risk factors obtained from genome-wide association studies (GWAS). Various combinations of machine learning models before and after feature selection were tested to develop the best classification model. The models were evaluated using area under the curve (AUC), calibration, and net reclassification improvement in the testing set. Results: The models without genetic liabilities achieved AUCs of 0.70 and 0.72 using the random forest and the neural network methods, respectively. Adding genetic liabilities improved the AUC for the random forest but not for the neural network. The best classification model was achieved when feature selection and classification were performed using random forest (AUC = 0.71, Spiegelhalter z score = 0.10, p-value = 0.92, calibration slope = 0.99). This model included genetic liabilities for total cholesterol and low-density lipoprotein (LDL). Conclusions: The study highlighted that incorporating genetic liabilities for lipids in a machine learning model may provide incremental value for hypertension classification beyond baseline characteristics.

Application of artificial intelligence in hypertension

Hypertension is an important modifiable risk factor for morbidity and mortality associated with cardiovascular disease. The incidence of hypertension is increasing not only in Korea but also in many Western countries due to the aging of the population and the increase in unhealthy lifestyles. However, hypertension control rates remain low due to poor adherence to antihypertensive medications, low awareness of hypertension, and numerous factors that contribute to hypertension, including diet, environment, lifestyle, obesity, and genetics. Because artificial intelligence (AI) involves data-driven algorithms, AI is an asset to understanding chronic diseases that are influenced by multiple factors, such as hypertension. Although several hypertension studies using AI have been published recently, most are exploratory descriptive studies that are often difficult for clinicians to understand and have little clinical relevance. This review aims to provide a clinician-centered perspective on AI by showing recent studies on the relevance of AI for patients with hypertension. The review is organized into sections on blood pressure measurement and hypertension diagnosis, prognosis, and management.

AI, Machine Learning, and ChatGPT in Hypertension

Hypertension, a leading cause of cardiovascular disease and premature death, remains incompletely understood despite extensive research. Indeed, even though numerous drugs are available, achieving adequate blood pressure control remains a challenge, prompting recent interest in artificial intelligence. To promote the use of machine learning in cardiovascular medicine, this review provides a brief introduction to machine learning and reviews its notable applications in hypertension management and research, such as disease diagnosis and prognosis, treatment decisions, and omics data analysis. The challenges and limitations associated with data-driven predictive techniques are also discussed. The goal of this review is to raise awareness and encourage the hypertension research community to consider machine learning as a key component in developing innovative diagnostic and therapeutic tools for hypertension. By integrating traditional cardiovascular risk factors with genomics, socioeconomic, behavioral, and environmental factors, machine learning may aid in the development of precise risk prediction models and personalized treatment approaches for patients with hypertension.

Predicting hypertension control using machine learning

Hypertension is a widely prevalent disease and uncontrolled hypertension predisposes affected individuals to severe adverse effects. Though the importance of controlling hypertension is clear, the multitude of therapeutic regimens and patient factors that affect the success of blood pressure control makes it difficult to predict the likelihood to predict whether a patient's blood pressure will be controlled. This project endeavors to investigate whether machine learning can accurately predict the control of a patient's hypertension within 12 months of a clinical encounter. To build the machine learning model, a retrospective review of the electronic medical records of 350,008 patients 18 years of age and older between January 1, 2015 and June 1, 2022 was performed to form model training and testing cohorts. The data included in the model included medication combinations, patient laboratory values, vital sign measurements, comorbidities, healthcare encounters, and demographic information. The mean age of the patient population was 65.6 years with 161,283 (46.1%) men and 275,001 (78.6%) white. A sliding time window of data was used to both prohibit data leakage from training sets to test sets and to maximize model performance. This sliding window resulted in using the study data to create 287 predictive models each using 2 years of training data and one week of testing data for a total study duration of five and a half years. Model performance was combined across all models. The primary outcome, prediction of blood pressure control within 12 months demonstrated an area under the curve of 0.76 (95% confidence interval; 0.75-0.76), sensitivity of 61.52% (61.0-62.03%), specificity of 75.69% (75.25-76.13%), positive predictive value of 67.75% (67.51-67.99%), and negative predictive value of 70.49% (70.32-70.66%). An AUC of 0.756 is considered to be moderately good for machine learning models. While the accuracy of this model is promising, it is impossible to state with certainty the clinical relevancy of any clinical support ML model without deploying it in a clinical setting and studying its impact on health outcomes. By also incorporating uncertainty analysis for every prediction, the authors believe that this approach offers the best-known solution to predicting hypertension control and that machine learning may be able to improve the accuracy of hypertension control predictions using patient information already available in the electronic health record. This method can serve as a foundation with further research to strengthen the model accuracy and to help determine clinical relevance.

Prognostic risk models for incident hypertension: A PRISMA systematic review and meta-analysis

OBJECTIVE

Our goal was to review the available literature on prognostic risk prediction for incident hypertension, synthesize performance, and provide suggestions for future work on the topic.

METHODS

A systematic search on PUBMED and Web of Science databases was conducted for studies on prognostic risk prediction models for incident hypertension in generally healthy individuals. Study-quality was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST) checklist. Three-level meta-analyses were used to obtain pooled AUC/C-statistic estimates. Heterogeneity was explored using study and cohort characteristics in meta-regressions.

RESULTS

From 5090 hits, we found 53 eligible studies, and included 47 in meta-analyses. Only four studies were assessed to have results with low risk of bias. Few models had been externally validated, with only the Framingham risk model validated more than thrice. The pooled AUC/C-statistics were 0.82 (0.77-0.86) for machine learning models and 0.78 (0.76-0.80) for traditional models, with high heterogeneity in both groups (I2 > 99%). Intra-class correlations within studies were 60% and 90%, respectively. Follow-up time (P = 0.0405) was significant for ML models and age (P = 0.0271) for traditional models in explaining heterogeneity. Validations of the Framingham risk model had high heterogeneity (I2 > 99%).

CONCLUSION

Overall, the quality of included studies was assessed as poor. AUC/C-statistic were mostly acceptable or good, and higher for ML models than traditional models. High heterogeneity implies large variability in the performance of new risk models. Further, large heterogeneity in validations of the Framingham risk model indicate variability in model performance on new populations. To enable researchers to assess hypertension risk models, we encourage adherence to existing guidelines for reporting and developing risk models, specifically reporting appropriate performance measures. Further, we recommend a stronger focus on validation of models by considering reasonable baseline models and performing external validations of existing models. Hence, developed risk models must be made available for external researchers.

Development of risk models of incident hypertension using machine learning on the HUNT study data

In this study, we aimed to create an 11-year hypertension risk prediction model using data from the Trøndelag Health (HUNT) Study in Norway, involving 17 852 individuals (20-85 years; 38% male; 24% incidence rate) with blood pressure (BP) below the hypertension threshold at baseline (1995-1997). We assessed 18 clinical, behavioral, and socioeconomic features, employing machine learning models such as eXtreme Gradient Boosting (XGBoost), Elastic regression, K-Nearest Neighbor, Support Vector Machines (SVM) and Random Forest. For comparison, we used logistic regression and a decision rule as reference models and validated six external models, with focus on the Framingham risk model. The top-performing models consistently included XGBoost, Elastic regression and SVM. These models efficiently identified hypertension risk, even among individuals with optimal baseline BP (< 120/80 mmHg), although improvement over reference models was modest. The recalibrated Framingham risk model outperformed the reference models, approaching the best-performing ML models. Important features included age, systolic and diastolic BP, body mass index, height, and family history of hypertension. In conclusion, our study demonstrated that linear effects sufficed for a well-performing model. The best models efficiently predicted hypertension risk, even among those with optimal or normal baseline BP, using few features. The recalibrated Framingham risk model proved effective in our cohort.

Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review

Cardiovascular diseases (CVDs) are significant health issues that result in high death rates globally. Early detection of cardiovascular events may lower the occurrence of acute myocardial infarction and reduce death rates in people with CVDs. Traditional data analysis is inadequate for managing multidimensional data related to the risk prediction of CVDs, heart attacks, medical image interpretations, therapeutic decision-making, and disease prognosis due to the complex pathological mechanisms and multiple factors involved. Artificial intelligence (AI) is a technology that utilizes advanced computer algorithms to extract information from large databases, and it has been integrated into the medical industry. AI methods have shown the ability to speed up the advancement of diagnosing and treating CVDs such as heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease, and more. In clinical settings, AI has shown usefulness in diagnosing cardiovascular illness, improving the efficiency of supporting tools, stratifying and categorizing diseases, and predicting outcomes. Advanced AI algorithms have been intricately designed to analyze intricate relationships within extensive healthcare data, enabling them to tackle more intricate jobs compared to conventional approaches.

Risk Assessment Score and Chi-Square Automatic Interaction Detection Algorithm for Hypertension Among Africans: Models From the SIREN Study

BACKGROUND

This study aimed to develop a risk-scoring model for hypertension among Africans.

METHODS

In this study, 4413 stroke-free controls were used to develop the risk-scoring model for hypertension. Logistic regression models were applied to 13 risk factors. We randomly split the dataset into training and testing data at a ratio of 80:20. Constant and standardized weights were assigned to factors significantly associated with hypertension in the regression model to develop a probability risk score on a scale of 0 to 1 using a logistic regression model. The model accuracy was assessed to estimate the cutoff score for discriminating hypertensives.

RESULTS

Mean age was 59.9±13.3 years, 56.0% were hypertensives, and 8 factors, including diabetes, age ≥65 years, higher waist circumference, (BMI) ≥30 kg/m2, lack of formal education, living in urban residence, family history of cardiovascular diseases, and dyslipidemia use were associated with hypertension. Cohen κ was maximal at ≥0.28, and a total probability risk score of ≥0.60 was adopted for both statistical weighting for risk quantification of hypertension in both datasets. The probability risk score presented a good performance-receiver operating characteristic: 64% (95% CI, 61.0-68.0), a sensitivity of 55.1%, specificity of 71.5%, positive predicted value of 70.9%, and negative predicted value of 55.8%, in the test dataset. Similarly, decision tree had a predictive accuracy of 67.7% (95% CI, 66.1-69.3) for the training set and 64.6% (95% CI, 61.0-68.0) for the testing dataset.

CONCLUSIONS

The novel risk-scoring model discriminated hypertensives with good accuracy and will be helpful in the early identification of community-based Africans vulnerable to hypertension for its primary prevention.

Digital hypertension towards to the anticipation medicine

"Digital Hypertension" is a new research field proposed by the Japanese Society of Hypertension that integrates digital technology into hypertension management and proactively promotes research activities. This novel approach includes the development of new technologies for better BP management, such as sensors for detecting environmental factors that affect BP, information processing, and machine learning. To facilitate "Digital Hypertension," a more sophisticated BP monitoring system capable of measuring an individual's BP more frequently in various situations would be required. With the use of these technologies, hypertension management could shift from the current "dots" management based on office BP readings during clinic visits to a "line" management system based on seamless home BP or individual BP data taken by a wearable BP monitoring device. DTx is the innovation to change hypertension management from "dots" to "line", completely achieved by wearable BP.

Disease Diagnosis Based on Improved Gray Wolf Optimization (IGWO) and Ensemble Classification

This paper introduces a simple strategy for diagnosing disease, which is called improved gray wolf optimization (IGWO) and ensemble classification. The proposed strategy consists of two sequential phases, which are; (i) Feature Selection Phase (FSP) and (ii) Ensemble Classification Phase (ECP). During the former, the most effective features for diagnosing disease are selected, while during the latter, the actual diagnosis takes place depending on voting of five different classifiers. The main contribution of this paper is a suggested modification for the traditional Gray Wolf Optimization (GWO), which is called Improved Gray Wolf Optimization (IGWO). As an optimization technique, the proposed IGWO is employed in the FSP for selecting the effective features. For evaluating, IGWO has been implemented using recent feature selection techniques as well as the proposed method. To accomplish the classification phase; ensemble classification has been used which uses several classification techniques such as; Naïve Bayes (NB), Support Vector Machines (SVM), Deep Neural Network (DNN), Decision Tree (DT), and K-Nearest Neighbors (KNN). Ensemble classification integrate several classifiers for improving prediction performance. Experimental results have shown that employing IGWO promotes the performance of the diagnosing strategy of different diseases in terms of precision, recall, and accuracy.

Risk-prediction model for incident hypertension in patients with obstructive sleep apnea based on SpO2 signals

This work proposes a method utilizing oxygen saturation (SpO2) for predicting incident hypertension in patients with obstructive sleep apnea (OSA). We extracted time domain features and frequency domain features from the SpO2 signal. For prediction, we employed several machine learning algorithms to establish the 3-year risk prediction model in the Chinese Sleep Health Study, including 250 subjects without baseline hypertension who underwent sleep monitoring. The proposed random forest model achieved an accuracy of 84.4%, a sensitivity of 77.0%, a specificity of 91.5% and an area under the receiver operator characteristic of 84.3% using 10-fold crossvalidation. We show that the model outperformed two sleep medicine specialists using clinical experience to predict hypertension. Furthermore, we applied the prediction results in the public Sleep Heart Health Study database and showed the subjects who were predicted to have hypertension would be at a higher risk in 4-6 years. This work shows the potential of SpO2 signal during sleep for the prediction of hypertension and could be beneficial to the early detection and timely treatment of hypertension in OSA patients.Clinical Relevance-There is no prediction model for incident hypertension in OSA patients in clinical practice. Most patients are unaware of health complexity, symptoms and risk factors before hypertension. Establishing an accurate prediction model can effectively provide early intervention for OSA patients and reduce the prevalence of hypertension.

Combating hypertension beyond genome-wide association studies: Microbiome and artificial intelligence as opportunities for precision medicine

The single largest contributor to human mortality is cardiovascular disease, the top risk factor for which is hypertension (HTN). The last two decades have placed much emphasis on the identification of genetic factors contributing to HTN. As a result, over 1,500 genetic alleles have been associated with human HTN. Mapping studies using genetic models of HTN have yielded hundreds of blood pressure (BP) loci but their individual effects on BP are minor, which limits opportunities to target them in the clinic. The value of collecting genome-wide association data is evident in ongoing research, which is beginning to utilize these data at individual-level genetic disparities combined with artificial intelligence (AI) strategies to develop a polygenic risk score (PRS) for the prediction of HTN. However, PRS alone may or may not be sufficient to account for the incidence and progression of HTN because genetics is responsible for <30% of the risk factors influencing the etiology of HTN pathogenesis. Therefore, integrating data from other nongenetic factors influencing BP regulation will be important to enhance the power of PRS. One such factor is the composition of gut microbiota, which constitute a more recently discovered important contributor to HTN. Studies to-date have clearly demonstrated that the transition from normal BP homeostasis to a state of elevated BP is linked to compositional changes in gut microbiota and its interaction with the host. Here, we first document evidence from studies on gut dysbiosis in animal models and patients with HTN followed by a discussion on the prospects of using microbiota data to develop a metagenomic risk score (MRS) for HTN to be combined with PRS and a clinical risk score (CRS). Finally, we propose that integrating AI to learn from the combined PRS, MRS and CRS may further enhance predictive power for the susceptibility and progression of HTN.

Survey and Evaluation of Hypertension Machine Learning Research

Background Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision-making. However, applications in different specialities are variable and generally limited. Like other conditions, the number of studies employing ML in hypertension research is growing rapidly. In this study, we aimed to survey hypertension research using ML, evaluate the reporting quality, and identify barriers to ML's potential to transform hypertension care. Methods and Results The Harmonious Understanding of Machine Learning Analytics Network survey questionnaire was applied to 63 hypertension-related ML research articles published between January 2019 and September 2021. The most common research topics were blood pressure prediction (38%), hypertension (22%), cardiovascular outcomes (6%), blood pressure variability (5%), treatment response (5%), and real-time blood pressure estimation (5%). The reporting quality of the articles was variable. Only 46% of articles described the study population or derivation cohort. Most articles (81%) reported at least 1 performance measure, but only 40% presented any measures of calibration. Compliance with ethics, patient privacy, and data security regulations were mentioned in 30 (48%) of the articles. Only 14% used geographically or temporally distinct validation data sets. Algorithmic bias was not addressed in any of the articles, with only 6 of them acknowledging risk of bias. Conclusions Recent ML research on hypertension is limited to exploratory research and has significant shortcomings in reporting quality, model validation, and algorithmic bias. Our analysis identifies areas for improvement that will help pave the way for the realization of the potential of ML in hypertension and facilitate its adoption.

Retinal vessel caliber and tortuosity and prediction of 5-year incidence of hypertension

PURPOSE

With arterial hypertension as a global risk factor for cerebrovascular and cardiovascular diseases, we examined whether retinal blood vessel caliber and tortuosity assessed by a vessel-constraint network model can predict the incidence of hypertension.

METHODS

The community-based prospective study included 9230 individuals who were followed for 5 years. Ocular fundus photographs taken at baseline were analyzed by a vessel-constraint network model.

RESULTS

Within the 5-year follow-up, 1279 (18.8%) and 474 (7.0%) participants out of 6813 individuals free of hypertension at baseline developed hypertension and severe hypertension, respectively. In multivariable analysis, a higher incidence of hypertension was related to a narrower retinal arteriolar diameter ( P  < 0.001), wider venular diameter ( P  = 0.005), and a smaller arteriole-to-venule diameter ratio ( P  < 0.001) at baseline. Individuals with the 5% narrowest arteriole or the 5% widest venule diameter had a 17.1-fold [95% confidence interval (CI):7.9, 37.2] or 2.3-fold (95% CI: 1.4, 3.7) increased risk for developing hypertension, as compared with those with the 5% widest arteriole or the 5% narrowest venule. The area under the receiver operator characteristic curve for predicting the 5-year incidence of hypertension and severe hypertension was 0.791 (95% CI: 0.778, 0.804) and 0.839 (95% CI: 0.821, 0.856), respectively. Although the venular tortuosity was positively associated with the presence of hypertension at baseline ( P  = 0.01), neither arteriolar tortuosity nor venular tortuosity was associated with incident hypertension (both P  ≥ 0.10).

CONCLUSION AND RELEVANCE

Narrower retinal arterioles and wider venules indicate an increased risk for incident hypertension within 5 years, while tortuous retinal venules are associated with the presence rather than the incidence of hypertension. The automatic assessment of retinal vessel features performed well in identifying individuals at risk of developing hypertension.

Artificial intelligence in cardiovascular prevention: new ways will open new doors

Prevention and effective treatment of cardiovascular disease are progressive issues that grow in tandem with the average age of the world population. Over recent decades, the potential role of artificial intelligence in cardiovascular medicine has been increasingly recognized because of the incredible amount of real-world data (RWD) regarding patient health status and healthcare delivery that can be collated from a variety of sources wherein patient information is routinely collected, including patient registries, clinical case reports, reimbursement claims and billing reports, medical devices, and electronic health records. Like any other (health) data, RWD can be analysed in accordance with high-quality research methods, and its analysis can deliver valuable patient-centric insights complementing the information obtained from conventional clinical trials. Artificial intelligence application on RWD has the potential to detect a patient's health trajectory leading to personalized medicine and tailored treatment. This article reviews the benefits of artificial intelligence in cardiovascular prevention and management, focusing on diagnostic and therapeutic improvements without neglecting the limitations of this new scientific approach.

Artificial Intelligence in Hypertension Management: An Ace up Your Sleeve

Arterial hypertension (AH) is a progressive issue that grows in importance with the increased average age of the world population. The potential role of artificial intelligence (AI) in its prevention and treatment is firmly recognized. Indeed, AI application allows personalized medicine and tailored treatment for each patient. Specifically, this article reviews the benefits of AI in AH management, pointing out diagnostic and therapeutic improvements without ignoring the limitations of this innovative scientific approach. Consequently, we conducted a detailed search on AI applications in AH: the articles (quantitative and qualitative) reviewed in this paper were obtained by searching journal databases such as PubMed and subject-specific professional websites, including Google Scholar. The search terms included artificial intelligence, artificial neural network, deep learning, machine learning, big data, arterial hypertension, blood pressure, blood pressure measurement, cardiovascular disease, and personalized medicine. Specifically, AI-based systems could help continuously monitor BP using wearable technologies; in particular, BP can be estimated from a photoplethysmograph (PPG) signal obtained from a smartphone or a smartwatch using DL. Furthermore, thanks to ML algorithms, it is possible to identify new hypertension genes for the early diagnosis of AH and the prevention of complications. Moreover, integrating AI with omics-based technologies will lead to the definition of the trajectory of the hypertensive patient and the use of the most appropriate drug. However, AI is not free from technical issues and biases, such as over/underfitting, the "black-box" nature of many ML algorithms, and patient data privacy. In conclusion, AI-based systems will change clinical practice for AH by identifying patient trajectories for new, personalized care plans and predicting patients' risks and necessary therapy adjustments due to changes in disease progression and/or therapy response.

Machine Learning in Hypertension Detection: A Study on World Hypertension Day Data

Many modifiable and non-modifiable risk factors have been associated with hypertension. However, current screening programs are still failing in identifying individuals at higher risk of hypertension. Given the major impact of high blood pressure on cardiovascular events and mortality, there is an urgent need to find new strategies to improve hypertension detection. We aimed to explore whether a machine learning (ML) algorithm can help identifying individuals predictors of hypertension. We analysed the data set generated by the questionnaires administered during the World Hypertension Day from 2015 to 2019. A total of 20206 individuals have been included for analysis. We tested five ML algorithms, exploiting different balancing techniques. Moreover, we computed the performance of the medical protocol currently adopted in the screening programs. Results show that a gain of sensitivity reflects in a loss of specificity, bringing to a scenario where there is not an algorithm and a configuration which properly outperforms against the others. However, Random Forest provides interesting performances (0.818 sensitivity - 0.629 specificity) compared with medical protocols (0.906 sensitivity - 0.230 specificity). Detection of hypertension at a population level still remains challenging and a machine learning approach could help in making screening programs more precise and cost effective, when based on accurate data collection. More studies are needed to identify new features to be acquired and to further improve the performances of ML models.

Predicting hypertension onset from longitudinal electronic health records with deep learning

Objective

Hypertension has long been recognized as one of the most important predisposing factors for cardiovascular diseases and mortality. In recent years, machine learning methods have shown potential in diagnostic and predictive approaches in chronic diseases. Electronic health records (EHRs) have emerged as a reliable source of longitudinal data. The aim of this study is to predict the onset of hypertension using modern deep learning (DL) architectures, specifically long short-term memory (LSTM) networks, and longitudinal EHRs.

Materials and Methods

We compare this approach to the best performing models reported from previous works, particularly XGboost, applied to aggregated features. Our work is based on data from 233 895 adult patients from a large health system in the United States. We divided our population into 2 distinct longitudinal datasets based on the diagnosis date. To ensure generalization to unseen data, we trained our models on the first dataset (dataset A "train and validation") using cross-validation, and then applied the models to a second dataset (dataset B "test") to assess their performance. We also experimented with 2 different time-windows before the onset of hypertension and evaluated the impact on model performance.

Results

With the LSTM network, we were able to achieve an area under the receiver operating characteristic curve value of 0.98 in the "train and validation" dataset A and 0.94 in the "test" dataset B for a prediction time window of 1 year. Lipid disorders, type 2 diabetes, and renal disorders are found to be associated with incident hypertension.

Conclusion

These findings show that DL models based on temporal EHR data can improve the identification of patients at high risk of hypertension and corresponding driving factors. In the long term, this work may support identifying individuals who are at high risk for developing hypertension and facilitate earlier intervention to prevent the future development of hypertension.

Machine Learning for Hypertension Prediction: a Systematic Review

PURPOSE OF REVIEW

To provide an overview of the literature regarding the use of machine learning algorithms to predict hypertension. A systematic review was performed to select recent articles on the subject.

RECENT FINDINGS

The screening of the articles was conducted using a machine learning algorithm (ASReview). A total of 21 articles published between January 2018 and May 2021 were identified and compared according to variable selection, train-test split, data balancing, outcome definition, final algorithm, and performance metrics. Overall, the articles achieved an area under the ROC curve (AUROC) between 0.766 and 1.00. The algorithms most frequently identified as having the best performance were support vector machines (SVM), extreme gradient boosting (XGBoost), and random forest. Machine learning algorithms are a promising tool to improve preventive clinical decisions and targeted public health policies for hypertension. However, technical factors such as outcome definition, availability of the final code, predictive performance, explainability, and data leakage need to be consistently and critically evaluated.

Evaluating the risk of hypertension in residents in primary care in Shanghai, China with machine learning algorithms

Objective

The prevention of hypertension in primary care requires an effective and suitable hypertension risk assessment model. The aim of this study was to develop and compare the performances of three machine learning algorithms in predicting the risk of hypertension for residents in primary care in Shanghai, China.

Methods

A dataset of 40,261 subjects over the age of 35 years was extracted from Electronic Healthcare Records of 47 community health centers from 2017 to 2019 in the Pudong district of Shanghai. Embedded methods were applied for feature selection. Machine learning algorithms, XGBoost, random forest, and logistic regression analyses were adopted in the process of model construction. The performance of models was evaluated by calculating the area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, accuracy and F1-score.

Results

The XGBoost model outperformed the other two models and achieved an AUC of 0.765 in the testing set. Twenty features were selected to construct the model, including age, diabetes status, urinary protein level, BMI, elderly health self-assessment, creatinine level, systolic blood pressure measured on the upper right arm, waist circumference, smoking status, low-density lipoprotein cholesterol level, high-density lipoprotein cholesterol level, frequency of drinking, glucose level, urea nitrogen level, total cholesterol level, diastolic blood pressure measured on the upper right arm, exercise frequency, time spent engaged in exercise, high salt consumption, and triglyceride level.

Conclusions

XGBoost outperformed random forest and logistic regression in predicting the risk of hypertension in primary care. The integration of this risk assessment model into primary care facilities may improve the prevention and management of hypertension in residents.

Development and validation of prediction models for hypertension risks: A cross-sectional study based on 4,287,407 participants

Objective

To develop an optimal screening model to identify the individuals with a high risk of hypertension in China by comparing tree-based machine learning models, such as classification and regression tree, random forest, adaboost with a decision tree, extreme gradient boosting decision tree, and other machine learning models like an artificial neural network, naive Bayes, and traditional logistic regression models.

Methods

A total of 4,287,407 adults participating in the national physical examination were included in the study. Features were selected using the least absolute shrinkage and selection operator regression. The Borderline synthetic minority over-sampling technique was used for data balance. Non-laboratory and semi-laboratory analyses were carried out in combination with the selected features. The tree-based machine learning models, other machine learning models, and traditional logistic regression models were constructed to identify individuals with hypertension, respectively. Top features selected using the best algorithm and the corresponding variable importance score were visualized.

Results

A total of 24 variables were finally included for analyses after the least absolute shrinkage and selection operator regression model. The sample size of hypertensive patients in the training set was expanded from 689,025 to 2,312,160 using the borderline synthetic minority over-sampling technique algorithm. The extreme gradient boosting decision tree algorithm showed the best results (area under the receiver operating characteristic curve of non-laboratory: 0.893 and area under the receiver operating characteristic curve of semi-laboratory: 0.894). This study found that age, systolic blood pressure, waist circumference, diastolic blood pressure, albumin, drinking frequency, electrocardiogram, ethnicity (uyghur, hui, and other), body mass index, sex (female), exercise frequency, diabetes mellitus, and total bilirubin are important factors reflecting hypertension. Besides, some algorithms included in the semi-laboratory analyses showed less improvement in the predictive performance compared to the non-laboratory analyses.

Conclusion

Using multiple methods, a more significant prediction model can be built, which discovers risk factors and provides new insights into the prediction and prevention of hypertension.

Opening the black box: interpretable machine learning for predictor finding of metabolic syndrome

OBJECTIVE

The internal workings ofmachine learning algorithms are complex and considered as low-interpretation "black box" models, making it difficult for domain experts to understand and trust these complex models. The study uses metabolic syndrome (MetS) as the entry point to analyze and evaluate the application value of model interpretability methods in dealing with difficult interpretation of predictive models.

METHODS

The study collects data from a chain of health examination institution in Urumqi from 2017 ~ 2019, and performs 39,134 remaining data after preprocessing such as deletion and filling. RFE is used for feature selection to reduce redundancy; MetS risk prediction models (logistic, random forest, XGBoost) are built based on a feature subset, and accuracy, sensitivity, specificity, Youden index, and AUROC value are used to evaluate the model classification performance; post-hoc model-agnostic interpretation methods (variable importance, LIME) are used to interpret the results of the predictive model.

RESULTS

Eighteen physical examination indicators are screened out by RFE, which can effectively solve the problem of physical examination data redundancy. Random forest and XGBoost models have higher accuracy, sensitivity, specificity, Youden index, and AUROC values compared with logistic regression. XGBoost models have higher sensitivity, Youden index, and AUROC values compared with random forest. The study uses variable importance, LIME and PDP for global and local interpretation of the optimal MetS risk prediction model (XGBoost), and different interpretation methods have different insights into the interpretation of model results, which are more flexible in model selection and can visualize the process and reasons for the model to make decisions. The interpretable risk prediction model in this study can help to identify risk factors associated with MetS, and the results showed that in addition to the traditional risk factors such as overweight and obesity, hyperglycemia, hypertension, and dyslipidemia, MetS was also associated with other factors, including age, creatinine, uric acid, and alkaline phosphatase.

CONCLUSION

The model interpretability methods are applied to the black box model, which can not only realize the flexibility of model application, but also make up for the uninterpretable defects of the model. Model interpretability methods can be used as a novel means of identifying variables that are more likely to be good predictors.

Machine Learning Approaches for Predicting Hypertension and Its Associated Factors Using Population-Level Data From Three South Asian Countries

Background

Hypertension is the most common modifiable risk factor for cardiovascular diseases in South Asia. Machine learning (ML) models have been shown to outperform clinical risk predictions compared to statistical methods, but studies using ML to predict hypertension at the population level are lacking. This study used ML approaches in a dataset of three South Asian countries to predict hypertension and its associated factors and compared the model's performances.

Methods

We conducted a retrospective study using ML analyses to detect hypertension using population-based surveys. We created a single dataset by harmonizing individual-level data from the most recent nationally representative Demographic and Health Survey in Bangladesh, Nepal, and India. The variables included blood pressure (BP), sociodemographic and economic factors, height, weight, hemoglobin, and random blood glucose. Hypertension was defined based on JNC-7 criteria. We applied six common ML-based classifiers: decision tree (DT), random forest (RF), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), logistic regression (LR), and linear discriminant analysis (LDA) to predict hypertension and its risk factors.

Results

Of the 8,18,603 participants, 82,748 (10.11%) had hypertension. ML models showed that significant factors for hypertension were age and BMI. Ever measured BP, education, taking medicine to lower BP, and doctor's perception of high BP was also significant but comparatively lower than age and BMI. XGBoost, GBM, LR, and LDA showed the highest accuracy score of 90%, RF and DT achieved 89 and 83%, respectively, to predict hypertension. DT achieved the precision value of 91%, and the rest performed with 90%. XGBoost, GBM, LR, and LDA achieved a recall value of 100%, RF scored 99%, and DT scored 90%. In F1-score, XGBoost, GBM, LR, and LDA scored 95%, while RF scored 94%, and DT scored 90%. All the algorithms performed with good and small log loss values &lt;6%.

Conclusion

ML models performed well to predict hypertension and its associated factors in South Asians. When employed on an open-source platform, these models are scalable to millions of people and might help individuals self-screen for hypertension at an early stage. Future studies incorporating biochemical markers are needed to improve the ML algorithms and evaluate them in real life.

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.

Is the Efficiency Score an Indicator for Incident Hypertension in the Community Population of Western China?

We aimed to explore the association between the efficiency score and the risk of hypertension. We conducted a prospective cohort study of 2412 adults aged 40 years or above without hypertension in Guizhou, China from 2010 to 2020. The data envelopment analysis input-oriented DEA-CCR model was used to calculate the efficiency scores. The Cox regression model was used to assess the relationship between the efficiency score and incident hypertension. The dose–response relationship was evaluated by restricted cubic spline. Quantile regression was used to analyze the effect of efficiency scores on SBP and DBP. A total of 857 new hypertension cases were identified with a mean follow-up of 6.88 years. The efficiency score was lower in the new hypertension cases than participants without hypertension (0.70 vs. 0.67). After adjusting for possible confounding factors, the HR of hypertension risk was 0.20 (95%CI: 0.09, 0.42) for per 0.1 increase in the efficiency score. The dose–response relationship showed a non-linear relationship between the efficiency score and hypertension risk. Our results showed that the efficiency score was a cost-effective tool to identify those at a high risk of hypertension, and suggested targeted preventive measures should be undertaken.

The Use of Machine Learning for the Care of Hypertension and Heart Failure

Machine learning (ML) is a branch of artificial intelligence that combines computer science, statistics, and decision theory to learn complex patterns from voluminous data. In the last decade, accumulating evidence has shown the utility of ML for prediction, diagnosis, and classification of hypertension and heart failure (HF). In addition, ML-enabled image analysis has potential value in assessing cardiac structure and function in an accurate, scalable, and efficient way. Considering the high burden of hypertension and HF in China and worldwide, ML may help address these challenges from different aspects. Indeed, prior studies have shown that ML can enhance each stage of patient care, from research and development, to daily clinical practice and population health. Through reviewing the published literature, the aims of the current systemic review are to summarize the utilities of ML for the care of those with hypertension and HF.

Patient-Level Cancer Prediction Models From a Nationwide Patient Cohort: Model Development and Validation

BACKGROUND

Nationwide population-based cohorts provide a new opportunity to build automated risk prediction models at the patient level, and claim data are one of the more useful resources to this end. To avoid unnecessary diagnostic intervention after cancer screening tests, patient-level prediction models should be developed.

OBJECTIVE

We aimed to develop cancer prediction models using nationwide claim databases with machine learning algorithms, which are explainable and easily applicable in real-world environments.

METHODS

As source data, we used the Korean National Insurance System Database. Every Korean in ≥40 years old undergoes a national health checkup every 2 years. We gathered all variables from the database including demographic information, basic laboratory values, anthropometric values, and previous medical history. We applied conventional logistic regression methods, light gradient boosting methods, neural networks, survival analysis, and one-class embedding classifier methods to effectively analyze high dimension data based on deep learning-based anomaly detection. Performance was measured with area under the curve and area under precision recall curve. We validated our models externally with a health checkup database from a tertiary hospital.

RESULTS

The one-class embedding classifier model received the highest area under the curve scores with values of 0.868, 0.849, 0.798, 0.746, 0.800, 0.749, and 0.790 for liver, lung, colorectal, pancreatic, gastric, breast, and cervical cancers, respectively. For area under precision recall curve, the light gradient boosting models had the highest score with values of 0.383, 0.401, 0.387, 0.300, 0.385, 0.357, and 0.296 for liver, lung, colorectal, pancreatic, gastric, breast, and cervical cancers, respectively.

CONCLUSIONS

Our results show that it is possible to easily develop applicable cancer prediction models with nationwide claim data using machine learning. The 7 models showed acceptable performances and explainability, and thus can be distributed easily in real-world environments.

Home Blood Pressure Monitoring: Current Status and New Developments

Home blood pressure monitoring (HBPM) is a reliable, convenient, and less costly alternative to ambulatory blood pressure monitoring (ABPM) for the diagnosis and management of hypertension. Recognition and use of HBPM have dramatically increased over the last 20 years and current guidelines make strong recommendations for the use of both HBPM and ABPM in patients with hypertension. The accuracy and reliability of home blood pressure (BP) measurements require use of a validated device and standardized procedures, and good patient information and training. Key HBPM parameters include morning BP, evening BP, and the morning-evening difference. In addition, newer semi-automatic HBPM devices can also measure nighttime BP at fixed intervals during sleep. Advances in technology mean that HBPM devices could provide additional relevant data (e.g., environmental conditions) or determine BP in response to a specific trigger (e.g., hypoxia, increased heart rate). The value of HBPM is highlighted by a growing body of evidence showing that home BP is an important predictor of target organ damage, and cardiovascular disease (CVD)- and stroke-related morbidity and mortality, and provides better prognostic information than office BP. In addition, use of HBPM to monitor antihypertensive therapy can help to optimize reductions in BP, improve BP control, and reduce target organ damage and cardiovascular risk. Overall, HBPM should play a central role in the management of patients with hypertension, with the goal of identifying increased risk and predicting the onset of CVD events, allowing proactive interventions to reduce risk and eliminate adverse outcomes.

From palpation of the pulse to cuff-free methods: evolution of arterial pressure measurement methods

The arterial pressure is an important physiological indicator. The review describes the different techniques of measurement of arterial pressure, their advantages and limitations. Moreover, it also represents a historical reference about the main stage of the development of clinical sphygmomanometrya that nowadays is a relevant method for measuring arterial pressure. The emergence and the development of devices for daily monitoring of arterial pressure and modern techniques for non-invasive arterial pressure measurement are described too.

Artificial Intelligence in Hypertension: Seeing Through a Glass Darkly

Hypertension remains the largest modifiable cause of mortality worldwide despite the availability of effective medications and sustained research efforts over the past 100 years. Hypertension requires transformative solutions that can help reduce the global burden of the disease. Artificial intelligence and machine learning, which have made a substantial impact on our everyday lives over the last decade may be the route to this transformation. However, artificial intelligence in health care is still in its nascent stages and realizing its potential requires numerous challenges to be overcome. In this review, we provide a clinician-centric perspective on artificial intelligence and machine learning as applied to medicine and hypertension. We focus on the main roadblocks impeding implementation of this technology in clinical care and describe efforts driving potential solutions. At the juncture, there is a critical requirement for clinical and scientific expertise to work in tandem with algorithmic innovation followed by rigorous validation and scrutiny to realize the promise of artificial intelligence-enabled health care for hypertension and other chronic diseases.